In multiple coordinated views (MCVs), visualizations across views update their content in response to users’ interactions in other views. Interactive systems provide direct manipulation to create coordination between views, but are restricted to limited types of predefined templates. By contrast, textual specification languages enable flexible coordination but expose technical burden. To bridge the gap, we contribute Nebula, a grammar based on natural language for coordinating visualizations in MCVs. The grammar design is informed by a novel framework based on a systematic review of 176 coordinations from existing theories and applications, which describes coordination by demonstration, i.e., how coordination is performed by users. With the framework, Nebula specification formalizes coordination as a composition of user- and coordination-triggered interactions in origin and destination views, respectively, along with potential data transformation between the interactions. We evaluate Nebula by demonstrating its expressiveness with a gallery of diverse examples and analyzing its usability on cognitive dimensions.

The invention discloses a declaration grammar-based two-layer visual linkage arrangement method and system. The method comprises the steps of abstracting interaction semantics of linkage to obtain anupper-layer framework, abstracting a data structure of linkage, obtaining a lower-layer framework, and establishing a conversion relationship between the two-layer frameworks; constructing an upper-layer arrangement grammar based on interactive operation based on the upper-layer framework, constructing a lower-layer arrangement grammar based on the data flow diagram based on the lower-layer framework, and establishing a conversion relationship of the two-layer arrangement grammar based on the conversion relationship of the two-layer framework; constructing a grammar parser according to the two-layer arrangement grammar and the conversion relationship thereof, constructing an upper-layer grammar parsing module according to the upper-layer arrangement grammar, constructing a lower-layer grammar parsing module according to the lower-layer arrangement grammar, and constructing a connection module between the two-layer grammar parsing modules according to the conversion relationship of thetwo-layer grammar; and obtaining a description code conforming to the upper-layer or lower-layer arrangement grammar, analyzing by using a grammar analyzer, and arranging visual linkage.

Air pollution has become a serious public health problem for many cities around the world. To find the causes of air pollution, the propagation processes of air pollutants must be studied at a large spatial scale. However, the complex and dynamic wind fields lead to highly uncertain pollutant transportation. The state-of-the-art data mining approaches cannot fully support the extensive analysis of such uncertain spatiotemporal propagation processes across multiple districts without the integration of domain knowledge. The limitation of these automated approaches motivates us to design and develop AirVis, a novel visual analytics system that assists domain experts in efficiently capturing and interpreting the uncertain propagation patterns of air pollution based on graph visualizations. Designing such a system poses three challenges: a) the extraction of propagation patterns; b) the scalability of pattern presentations; and c) the analysis of propagation processes. To address these challenges, we develop a novel pattern mining framework to model pollutant transportation and extract frequent propagation patterns efficiently from large-scale atmospheric data. Furthermore, we organize the extracted patterns hierarchically based on the minimum description length (MDL) principle and empower expert users to explore and analyze these patterns effectively on the basis of pattern topologies. We demonstrated the effectiveness of our approach through two case studies conducted with a real-world dataset and positive feedback from domain experts.